Synthetic Aperture Radar High Resolution Imaging when Performing Random Nonrepeating Radar Orbits

While synthetic aperture radar (SAR) coherent change detection (CCD) is a sensitive measure of change with high tactical value, it requires precise, repeated data collections for which a steady, predictable airborne flight trajectory may endanger the air vehicle in a high-threat environment. CCD performance is defined by the collection overlap. For identifying changes between collections without similarity, from randomized, non-repeating trajectories, a new approach is proposed where no overlap is necessary. IERUS will leverage its previous work to generate models of objects and other machine learning techniques to match objects within the SAR image while accounting for the perspective change between collections. Additional efforts will use collection geometry or multiple collections to reduce false alarms. Later program phases will integrate this change detection along with real-time image formation for a real-time, tactical product to make the most of the data collection given the air vehicle maneuvers necessary to maximize survival in a high-threat environment.